Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations8998
Missing cells552
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
Average record size in memory122.0 B

Variable types

Numeric11
Boolean1
Categorical4

Alerts

age is highly overall correlated with dependents and 4 other fieldsHigh correlation
income is highly overall correlated with age and 4 other fieldsHigh correlation
frq is highly overall correlated with age and 4 other fieldsHigh correlation
mnt is highly overall correlated with age and 4 other fieldsHigh correlation
clothes is highly overall correlated with house_keeping and 3 other fieldsHigh correlation
kitchen is highly overall correlated with clothes and 2 other fieldsHigh correlation
small_appliances is highly overall correlated with clothesHigh correlation
toys is highly overall correlated with clothes and 2 other fieldsHigh correlation
house_keeping is highly overall correlated with clothes and 2 other fieldsHigh correlation
per_net_purchase is highly overall correlated with age and 4 other fieldsHigh correlation
dependents is highly overall correlated with age and 4 other fieldsHigh correlation
dependents has 282 (3.1%) missing values Missing
status has 177 (2.0%) missing values Missing
kitchen has 833 (9.3%) zeros Zeros
toys has 815 (9.1%) zeros Zeros
house_keeping has 851 (9.5%) zeros Zeros

Reproduction

Analysis started2025-10-01 12:48:27.576611
Analysis finished2025-10-01 12:48:32.310699
Duration4.73 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

age
Real number (ℝ)

High correlation 

Distinct61
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1966.0597
Minimum1936
Maximum1996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-10-01T13:48:32.341442image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1936
5-th percentile1939
Q11951
median1966
Q31981
95-th percentile1993
Maximum1996
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.296552
Coefficient of variation (CV)0.0087975723
Kurtosis-1.1959901
Mean1966.0597
Median Absolute Deviation (MAD)15
Skewness0.0079540843
Sum17690605
Variance299.17072
MonotonicityNot monotonic
2025-10-01T13:48:32.383007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1974 171
 
1.9%
1951 169
 
1.9%
1992 168
 
1.9%
1979 167
 
1.9%
1976 166
 
1.8%
1961 165
 
1.8%
1960 164
 
1.8%
1949 163
 
1.8%
1959 162
 
1.8%
1978 160
 
1.8%
Other values (51) 7343
81.6%
ValueCountFrequency (%)
1936 75
0.8%
1937 140
1.6%
1938 146
1.6%
1939 137
1.5%
1940 153
1.7%
1941 143
1.6%
1942 155
1.7%
1943 142
1.6%
1944 152
1.7%
1945 142
1.6%
ValueCountFrequency (%)
1996 81
0.9%
1995 147
1.6%
1994 159
1.8%
1993 157
1.7%
1992 168
1.9%
1991 141
1.6%
1990 133
1.5%
1989 152
1.7%
1988 150
1.7%
1987 136
1.5%

income
Real number (ℝ)

High correlation 

Distinct8524
Distinct (%)95.2%
Missing46
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean69963.551
Minimum10000
Maximum140628
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-10-01T13:48:32.423183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile26314.6
Q147741
median70030.5
Q392218
95-th percentile113395.3
Maximum140628
Range130628
Interquartile range (IQR)44477

Descriptive statistics

Standard deviation27591.556
Coefficient of variation (CV)0.39437044
Kurtosis-0.92932804
Mean69963.551
Median Absolute Deviation (MAD)22214.5
Skewness0.0086888909
Sum6.2631371 × 108
Variance7.6129397 × 108
MonotonicityNot monotonic
2025-10-01T13:48:32.467053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 35
 
0.4%
64185 4
 
< 0.1%
49948 3
 
< 0.1%
37902 3
 
< 0.1%
66184 3
 
< 0.1%
99452 3
 
< 0.1%
39782 3
 
< 0.1%
83455 3
 
< 0.1%
83267 3
 
< 0.1%
51743 3
 
< 0.1%
Other values (8514) 8889
98.8%
(Missing) 46
 
0.5%
ValueCountFrequency (%)
10000 35
0.4%
10182 1
 
< 0.1%
10186 1
 
< 0.1%
10608 1
 
< 0.1%
10886 1
 
< 0.1%
11347 1
 
< 0.1%
11437 1
 
< 0.1%
11474 1
 
< 0.1%
11719 1
 
< 0.1%
11760 1
 
< 0.1%
ValueCountFrequency (%)
140628 1
< 0.1%
137338 1
< 0.1%
137053 1
< 0.1%
136922 1
< 0.1%
136213 1
< 0.1%
136192 1
< 0.1%
135959 1
< 0.1%
135789 1
< 0.1%
135579 1
< 0.1%
134689 1
< 0.1%

frq
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.848077
Minimum3
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-10-01T13:48:32.507302image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q110
median17
Q328
95-th percentile40
Maximum59
Range56
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.903435
Coefficient of variation (CV)0.54934462
Kurtosis-0.41398892
Mean19.848077
Median Absolute Deviation (MAD)8
Skewness0.69777907
Sum178593
Variance118.88489
MonotonicityNot monotonic
2025-10-01T13:48:32.548812image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 635
 
7.1%
9 583
 
6.5%
11 513
 
5.7%
8 493
 
5.5%
12 418
 
4.6%
7 325
 
3.6%
13 316
 
3.5%
14 282
 
3.1%
21 238
 
2.6%
16 233
 
2.6%
Other values (47) 4962
55.1%
ValueCountFrequency (%)
3 5
 
0.1%
4 24
 
0.3%
5 87
 
1.0%
6 173
 
1.9%
7 325
3.6%
8 493
5.5%
9 583
6.5%
10 635
7.1%
11 513
5.7%
12 418
4.6%
ValueCountFrequency (%)
59 2
 
< 0.1%
58 1
 
< 0.1%
57 1
 
< 0.1%
56 3
 
< 0.1%
55 3
 
< 0.1%
54 3
 
< 0.1%
53 6
 
0.1%
52 8
0.1%
51 15
0.2%
50 9
0.1%

rcn
Real number (ℝ)

Distinct378
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.469771
Minimum0
Maximum549
Zeros44
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-10-01T13:48:32.589845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q126
median53
Q379
95-th percentile99
Maximum549
Range549
Interquartile range (IQR)53

Descriptive statistics

Standard deviation69.761802
Coefficient of variation (CV)1.116729
Kurtosis21.096923
Mean62.469771
Median Absolute Deviation (MAD)26
Skewness4.1740066
Sum562103
Variance4866.709
MonotonicityNot monotonic
2025-10-01T13:48:32.629053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 107
 
1.2%
56 105
 
1.2%
64 103
 
1.1%
29 102
 
1.1%
4 102
 
1.1%
27 100
 
1.1%
92 100
 
1.1%
68 99
 
1.1%
54 99
 
1.1%
17 98
 
1.1%
Other values (368) 7983
88.7%
ValueCountFrequency (%)
0 44
0.5%
1 91
1.0%
2 92
1.0%
3 91
1.0%
4 102
1.1%
5 69
0.8%
6 86
1.0%
7 80
0.9%
8 77
0.9%
9 107
1.2%
ValueCountFrequency (%)
549 3
< 0.1%
547 1
 
< 0.1%
546 3
< 0.1%
542 2
< 0.1%
540 1
 
< 0.1%
538 1
 
< 0.1%
537 1
 
< 0.1%
535 1
 
< 0.1%
534 1
 
< 0.1%
533 1
 
< 0.1%

mnt
Real number (ℝ)

High correlation 

Distinct717
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean622.16281
Minimum6
Maximum3052
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-10-01T13:48:32.668711image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q163
median383
Q31076
95-th percentile1917.15
Maximum3052
Range3046
Interquartile range (IQR)1013

Descriptive statistics

Standard deviation646.7682
Coefficient of variation (CV)1.0395482
Kurtosis-0.058093769
Mean622.16281
Median Absolute Deviation (MAD)343
Skewness0.9809806
Sum5598221
Variance418309.11
MonotonicityNot monotonic
2025-10-01T13:48:32.708696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 158
 
1.8%
41 121
 
1.3%
20 108
 
1.2%
40 89
 
1.0%
42 88
 
1.0%
64 86
 
1.0%
66 78
 
0.9%
65 76
 
0.8%
92 61
 
0.7%
118 56
 
0.6%
Other values (707) 8077
89.8%
ValueCountFrequency (%)
6 1
 
< 0.1%
7 2
 
< 0.1%
8 8
 
0.1%
9 14
 
0.2%
10 22
0.2%
11 26
0.3%
12 23
0.3%
13 28
0.3%
14 37
0.4%
15 26
0.3%
ValueCountFrequency (%)
3052 1
< 0.1%
2938 1
< 0.1%
2936 1
< 0.1%
2878 1
< 0.1%
2823 1
< 0.1%
2821 1
< 0.1%
2705 2
< 0.1%
2704 2
< 0.1%
2703 1
< 0.1%
2648 2
< 0.1%

clothes
Real number (ℝ)

High correlation 

Distinct99
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.446655
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-10-01T13:48:32.750668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11
Q133
median51
Q369
95-th percentile88
Maximum99
Range98
Interquartile range (IQR)36

Descriptive statistics

Standard deviation23.422249
Coefficient of variation (CV)0.46429737
Kurtosis-0.91859542
Mean50.446655
Median Absolute Deviation (MAD)18
Skewness-0.078219313
Sum453919
Variance548.60174
MonotonicityNot monotonic
2025-10-01T13:48:32.792675image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
55 150
 
1.7%
40 141
 
1.6%
47 139
 
1.5%
41 139
 
1.5%
70 137
 
1.5%
56 136
 
1.5%
58 136
 
1.5%
46 135
 
1.5%
57 133
 
1.5%
31 133
 
1.5%
Other values (89) 7619
84.7%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 15
 
0.2%
3 24
 
0.3%
4 38
0.4%
5 44
0.5%
6 53
0.6%
7 55
0.6%
8 57
0.6%
9 70
0.8%
10 64
0.7%
ValueCountFrequency (%)
99 1
 
< 0.1%
98 1
 
< 0.1%
97 9
 
0.1%
96 17
 
0.2%
95 25
 
0.3%
94 36
0.4%
93 56
0.6%
92 46
0.5%
91 60
0.7%
90 64
0.7%

kitchen
Real number (ℝ)

High correlation  Zeros 

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0396755
Minimum0
Maximum75
Zeros833
Zeros (%)9.3%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-10-01T13:48:32.831451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310
95-th percentile23
Maximum75
Range75
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.8481393
Coefficient of variation (CV)1.1148439
Kurtosis5.619266
Mean7.0396755
Median Absolute Deviation (MAD)3
Skewness2.0494582
Sum63343
Variance61.593291
MonotonicityNot monotonic
2025-10-01T13:48:32.898065image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1326
14.7%
2 1005
11.2%
0 833
 
9.3%
3 761
 
8.5%
4 644
 
7.2%
5 584
 
6.5%
6 506
 
5.6%
7 404
 
4.5%
8 372
 
4.1%
10 289
 
3.2%
Other values (48) 2274
25.3%
ValueCountFrequency (%)
0 833
9.3%
1 1326
14.7%
2 1005
11.2%
3 761
8.5%
4 644
7.2%
5 584
6.5%
6 506
 
5.6%
7 404
 
4.5%
8 372
 
4.1%
9 277
 
3.1%
ValueCountFrequency (%)
75 1
< 0.1%
67 1
< 0.1%
65 1
< 0.1%
61 1
< 0.1%
59 1
< 0.1%
58 1
< 0.1%
55 1
< 0.1%
51 1
< 0.1%
50 1
< 0.1%
49 2
< 0.1%

small_appliances
Real number (ℝ)

High correlation 

Distinct73
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.524116
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-10-01T13:48:32.940904image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q119
median28
Q337
95-th percentile50
Maximum74
Range73
Interquartile range (IQR)18

Descriptive statistics

Standard deviation12.586437
Coefficient of variation (CV)0.44125597
Kurtosis-0.42300302
Mean28.524116
Median Absolute Deviation (MAD)9
Skewness0.31464565
Sum256660
Variance158.41839
MonotonicityNot monotonic
2025-10-01T13:48:32.986171image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 286
 
3.2%
22 277
 
3.1%
26 276
 
3.1%
27 269
 
3.0%
19 268
 
3.0%
25 264
 
2.9%
30 262
 
2.9%
28 261
 
2.9%
31 254
 
2.8%
29 236
 
2.6%
Other values (63) 6345
70.5%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 2
 
< 0.1%
3 13
 
0.1%
4 39
 
0.4%
5 53
0.6%
6 61
0.7%
7 79
0.9%
8 104
1.2%
9 131
1.5%
10 125
1.4%
ValueCountFrequency (%)
74 2
 
< 0.1%
73 1
 
< 0.1%
72 1
 
< 0.1%
70 1
 
< 0.1%
69 3
 
< 0.1%
68 1
 
< 0.1%
67 1
 
< 0.1%
66 7
0.1%
65 4
 
< 0.1%
64 10
0.1%

toys
Real number (ℝ)

High correlation  Zeros 

Distinct58
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0368971
Minimum0
Maximum62
Zeros815
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-10-01T13:48:33.029577image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q310
95-th percentile23
Maximum62
Range62
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.9244217
Coefficient of variation (CV)1.1261244
Kurtosis5.6446572
Mean7.0368971
Median Absolute Deviation (MAD)3
Skewness2.0960474
Sum63318
Variance62.79646
MonotonicityNot monotonic
2025-10-01T13:48:33.070900image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1370
15.2%
2 988
11.0%
0 815
 
9.1%
3 779
 
8.7%
4 675
 
7.5%
5 542
 
6.0%
6 499
 
5.5%
7 409
 
4.5%
8 344
 
3.8%
9 295
 
3.3%
Other values (48) 2282
25.4%
ValueCountFrequency (%)
0 815
9.1%
1 1370
15.2%
2 988
11.0%
3 779
8.7%
4 675
7.5%
5 542
 
6.0%
6 499
 
5.5%
7 409
 
4.5%
8 344
 
3.8%
9 295
 
3.3%
ValueCountFrequency (%)
62 1
 
< 0.1%
61 1
 
< 0.1%
60 2
< 0.1%
57 1
 
< 0.1%
56 1
 
< 0.1%
54 2
< 0.1%
52 3
< 0.1%
50 4
< 0.1%
49 1
 
< 0.1%
48 1
 
< 0.1%

house_keeping
Real number (ℝ)

High correlation  Zeros 

Distinct59
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9299844
Minimum0
Maximum77
Zeros851
Zeros (%)9.5%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-10-01T13:48:33.111430image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q39
95-th percentile23
Maximum77
Range77
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.8826554
Coefficient of variation (CV)1.1374709
Kurtosis6.8855217
Mean6.9299844
Median Absolute Deviation (MAD)3
Skewness2.2291241
Sum62356
Variance62.136255
MonotonicityNot monotonic
2025-10-01T13:48:33.155053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1326
14.7%
2 981
10.9%
0 851
 
9.5%
3 848
 
9.4%
4 675
 
7.5%
5 519
 
5.8%
6 477
 
5.3%
7 446
 
5.0%
8 357
 
4.0%
9 309
 
3.4%
Other values (49) 2209
24.5%
ValueCountFrequency (%)
0 851
9.5%
1 1326
14.7%
2 981
10.9%
3 848
9.4%
4 675
7.5%
5 519
 
5.8%
6 477
 
5.3%
7 446
 
5.0%
8 357
 
4.0%
9 309
 
3.4%
ValueCountFrequency (%)
77 1
 
< 0.1%
72 1
 
< 0.1%
62 1
 
< 0.1%
59 1
 
< 0.1%
58 2
< 0.1%
57 2
< 0.1%
56 1
 
< 0.1%
55 3
< 0.1%
52 2
< 0.1%
50 1
 
< 0.1%

dependents
Boolean

High correlation  Missing 

Distinct2
Distinct (%)< 0.1%
Missing282
Missing (%)3.1%
Memory size17.7 KiB
True
6164 
False
2552 
(Missing)
 
282
ValueCountFrequency (%)
True 6164
68.5%
False 2552
28.4%
(Missing) 282
 
3.1%
2025-10-01T13:48:33.193079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

per_net_purchase
Real number (ℝ)

High correlation 

Distinct82
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.428984
Minimum4
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size70.4 KiB
2025-10-01T13:48:33.347014image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile11
Q128
median45
Q357
95-th percentile69
Maximum88
Range84
Interquartile range (IQR)29

Descriptive statistics

Standard deviation18.495742
Coefficient of variation (CV)0.43592235
Kurtosis-1.0346606
Mean42.428984
Median Absolute Deviation (MAD)14
Skewness-0.26645322
Sum381776
Variance342.09249
MonotonicityNot monotonic
2025-10-01T13:48:33.389053image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
56 215
 
2.4%
54 214
 
2.4%
57 212
 
2.4%
55 199
 
2.2%
58 192
 
2.1%
61 192
 
2.1%
53 189
 
2.1%
13 188
 
2.1%
60 185
 
2.1%
59 184
 
2.0%
Other values (72) 7028
78.1%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 3
 
< 0.1%
6 15
 
0.2%
7 35
 
0.4%
8 54
 
0.6%
9 88
1.0%
10 124
1.4%
11 148
1.6%
12 166
1.8%
13 188
2.1%
ValueCountFrequency (%)
88 1
 
< 0.1%
84 1
 
< 0.1%
83 1
 
< 0.1%
82 3
 
< 0.1%
81 3
 
< 0.1%
80 4
 
< 0.1%
79 7
 
0.1%
78 12
0.1%
77 13
0.1%
76 23
0.3%

gender
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
M
5784 
F
3214 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8998
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowF
3rd rowM
4th rowF
5th rowF

Common Values

ValueCountFrequency (%)
M 5784
64.3%
F 3214
35.7%

Length

2025-10-01T13:48:33.425657image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T13:48:33.456208image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
m 5784
64.3%
f 3214
35.7%

Most occurring characters

ValueCountFrequency (%)
M 5784
64.3%
F 3214
35.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 8998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
M 5784
64.3%
F 3214
35.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 8998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
M 5784
64.3%
F 3214
35.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 8998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
M 5784
64.3%
F 3214
35.7%

education
Categorical

Distinct6
Distinct (%)0.1%
Missing47
Missing (%)0.5%
Memory size70.4 KiB
Graduation
4429 
2nd Cycle
1496 
Master
1303 
1st Cycle
1104 
PhD
593 

Length

Max length10
Median length9
Mean length8.6605966
Min length3

Characters and Unicode

Total characters77521
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGraduation
2nd rowPhD
3rd rowGraduation
4th rowMaster
5th rowGraduation

Common Values

ValueCountFrequency (%)
Graduation 4429
49.2%
2nd Cycle 1496
 
16.6%
Master 1303
 
14.5%
1st Cycle 1104
 
12.3%
PhD 593
 
6.6%
OldSchool 26
 
0.3%
(Missing) 47
 
0.5%

Length

2025-10-01T13:48:33.505966image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T13:48:33.624644image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
graduation 4429
38.3%
cycle 2600
22.5%
2nd 1496
 
13.0%
master 1303
 
11.3%
1st 1104
 
9.6%
phd 593
 
5.1%
oldschool 26
 
0.2%

Most occurring characters

ValueCountFrequency (%)
a 10161
13.1%
t 6836
 
8.8%
d 5951
 
7.7%
n 5925
 
7.6%
r 5732
 
7.4%
o 4481
 
5.8%
G 4429
 
5.7%
u 4429
 
5.7%
i 4429
 
5.7%
e 3903
 
5.0%
Other values (14) 21245
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 77521
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10161
13.1%
t 6836
 
8.8%
d 5951
 
7.7%
n 5925
 
7.6%
r 5732
 
7.4%
o 4481
 
5.8%
G 4429
 
5.7%
u 4429
 
5.7%
i 4429
 
5.7%
e 3903
 
5.0%
Other values (14) 21245
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 77521
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10161
13.1%
t 6836
 
8.8%
d 5951
 
7.7%
n 5925
 
7.6%
r 5732
 
7.4%
o 4481
 
5.8%
G 4429
 
5.7%
u 4429
 
5.7%
i 4429
 
5.7%
e 3903
 
5.0%
Other values (14) 21245
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 77521
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10161
13.1%
t 6836
 
8.8%
d 5951
 
7.7%
n 5925
 
7.6%
r 5732
 
7.4%
o 4481
 
5.8%
G 4429
 
5.7%
u 4429
 
5.7%
i 4429
 
5.7%
e 3903
 
5.0%
Other values (14) 21245
27.4%

status
Categorical

Missing 

Distinct6
Distinct (%)0.1%
Missing177
Missing (%)2.0%
Memory size70.4 KiB
Married
3273 
Single
2293 
Together
2118 
Divorced
677 
Widow
445 

Length

Max length8
Median length7
Mean length6.9577145
Min length5

Characters and Unicode

Total characters61374
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTogether
2nd rowDivorced
3rd rowMarried
4th rowTogether
5th rowSingle

Common Values

ValueCountFrequency (%)
Married 3273
36.4%
Single 2293
25.5%
Together 2118
23.5%
Divorced 677
 
7.5%
Widow 445
 
4.9%
Whatever 15
 
0.2%
(Missing) 177
 
2.0%

Length

2025-10-01T13:48:33.674674image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T13:48:33.718439image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
married 3273
37.1%
single 2293
26.0%
together 2118
24.0%
divorced 677
 
7.7%
widow 445
 
5.0%
whatever 15
 
0.2%

Most occurring characters

ValueCountFrequency (%)
e 10509
17.1%
r 9356
15.2%
i 6688
10.9%
g 4411
 
7.2%
d 4395
 
7.2%
a 3288
 
5.4%
M 3273
 
5.3%
o 3240
 
5.3%
l 2293
 
3.7%
n 2293
 
3.7%
Other values (9) 11628
18.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61374
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 10509
17.1%
r 9356
15.2%
i 6688
10.9%
g 4411
 
7.2%
d 4395
 
7.2%
a 3288
 
5.4%
M 3273
 
5.3%
o 3240
 
5.3%
l 2293
 
3.7%
n 2293
 
3.7%
Other values (9) 11628
18.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61374
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 10509
17.1%
r 9356
15.2%
i 6688
10.9%
g 4411
 
7.2%
d 4395
 
7.2%
a 3288
 
5.4%
M 3273
 
5.3%
o 3240
 
5.3%
l 2293
 
3.7%
n 2293
 
3.7%
Other values (9) 11628
18.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61374
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 10509
17.1%
r 9356
15.2%
i 6688
10.9%
g 4411
 
7.2%
d 4395
 
7.2%
a 3288
 
5.4%
M 3273
 
5.3%
o 3240
 
5.3%
l 2293
 
3.7%
n 2293
 
3.7%
Other values (9) 11628
18.9%

description
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size70.4 KiB
OK nice!
3434 
Meh...
2107 
Kind of OK
2090 
Take my money!!
1326 
Horrible
 
41

Length

Max length15
Median length10
Mean length9.027784
Min length6

Characters and Unicode

Total characters81232
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTake my money!!
2nd rowTake my money!!
3rd rowKind of OK
4th rowOK nice!
5th rowTake my money!!

Common Values

ValueCountFrequency (%)
OK nice! 3434
38.2%
Meh... 2107
23.4%
Kind of OK 2090
23.2%
Take my money!! 1326
 
14.7%
Horrible 41
 
0.5%

Length

2025-10-01T13:48:33.762530image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-01T13:48:33.796488image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ValueCountFrequency (%)
ok 5524
28.7%
nice 3434
17.8%
meh 2107
 
10.9%
kind 2090
 
10.8%
of 2090
 
10.8%
take 1326
 
6.9%
my 1326
 
6.9%
money 1326
 
6.9%
horrible 41
 
0.2%

Most occurring characters

ValueCountFrequency (%)
10266
12.6%
e 8234
10.1%
K 7614
9.4%
n 6850
 
8.4%
. 6321
 
7.8%
! 6086
 
7.5%
i 5565
 
6.9%
O 5524
 
6.8%
o 3457
 
4.3%
c 3434
 
4.2%
Other values (13) 17881
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 81232
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
10266
12.6%
e 8234
10.1%
K 7614
9.4%
n 6850
 
8.4%
. 6321
 
7.8%
! 6086
 
7.5%
i 5565
 
6.9%
O 5524
 
6.8%
o 3457
 
4.3%
c 3434
 
4.2%
Other values (13) 17881
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 81232
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
10266
12.6%
e 8234
10.1%
K 7614
9.4%
n 6850
 
8.4%
. 6321
 
7.8%
! 6086
 
7.5%
i 5565
 
6.9%
O 5524
 
6.8%
o 3457
 
4.3%
c 3434
 
4.2%
Other values (13) 17881
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 81232
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
10266
12.6%
e 8234
10.1%
K 7614
9.4%
n 6850
 
8.4%
. 6321
 
7.8%
! 6086
 
7.5%
i 5565
 
6.9%
O 5524
 
6.8%
o 3457
 
4.3%
c 3434
 
4.2%
Other values (13) 17881
22.0%

Interactions

2025-10-01T13:48:31.840609image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:27.872478image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.635690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.976923image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.374296image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.694799image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.097803image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.416692image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.749428image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.081071image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.503566image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.867838image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:27.901724image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.665580image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.008600image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.403057image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.723682image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.126528image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.446783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.779516image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.109718image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.534129image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.897783image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:27.933111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.695771image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.083794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.433353image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.754344image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.156635image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.477508image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.810860image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.139728image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.565181image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.925331image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:27.961800image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.726025image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.120694image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.462253image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.782690image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.185293image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.506255image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.840173image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.168013image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.595088image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.952570image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:27.990632image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.754840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.152929image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.489866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.889371image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.212562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.534943image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.869637image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.198118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.624491image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.980841image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.019840image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.785415image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.186379image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.518589image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.919007image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.242090image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.565407image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.899576image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.227684image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.656093image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:32.008445image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.048536image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.814593image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.221683image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.546649image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.947533image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.270060image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.594364image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.928743image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.256341image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.685794image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:32.037673image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.516778image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.846311image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.253989image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.576978image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.978203image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.300405image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.625402image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.959866image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.286320image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.717348image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:32.067814image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.547474image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.879120image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.285000image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.607079image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.009668image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.330035image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.657183image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.990620image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.317569image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.749111image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:32.096172image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.576725image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.913101image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.314559image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.635845image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.038118image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.359451image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.688420image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.020562image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.346650image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.779519image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:32.126920image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.608640image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:28.947523image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.346072image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:29.667233image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.070408image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.390175image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:30.720477image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.053239image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.475898image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
2025-10-01T13:48:31.812126image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/

Correlations

2025-10-01T13:48:33.826925image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ageincomefrqrcnmntclotheskitchensmall_appliancestoyshouse_keepingper_net_purchase
age1.000-0.934-0.7660.178-0.814-0.2520.258-0.0210.2560.2670.786
income-0.9341.0000.813-0.1820.8500.234-0.2410.021-0.238-0.248-0.734
frq-0.7660.8131.000-0.1930.9770.129-0.1590.066-0.161-0.167-0.624
rcn0.178-0.182-0.1931.000-0.158-0.0820.0560.0380.0640.0630.138
mnt-0.8140.8500.977-0.1581.0000.066-0.1250.120-0.127-0.135-0.735
clothes-0.2520.2340.129-0.0820.0661.000-0.660-0.628-0.657-0.6540.034
kitchen0.258-0.241-0.1590.056-0.125-0.6601.0000.0850.4170.4150.035
small_appliances-0.0210.0210.0660.0380.120-0.6280.0851.0000.0940.092-0.143
toys0.256-0.238-0.1610.064-0.127-0.6570.4170.0941.0000.3850.037
house_keeping0.267-0.248-0.1670.063-0.135-0.6540.4150.0920.3851.0000.053
per_net_purchase0.786-0.734-0.6240.138-0.7350.0340.035-0.1430.0370.0531.000
2025-10-01T13:48:33.875677image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
ageclothesdependentsdescriptioneducationfrqgenderhouse_keepingincomekitchenmntper_net_purchasercnsmall_appliancesstatustoys
age1.000-0.2280.6920.2580.181-0.7630.0000.173-0.9380.171-0.8490.7770.071-0.0260.2080.161
clothes-0.2281.0000.2710.0590.2470.1530.000-0.7160.213-0.7200.1680.017-0.027-0.6480.125-0.716
dependents0.6920.2711.0000.3750.0000.5410.0000.0970.5770.1130.6150.7110.0820.2550.1900.129
description0.2580.0590.3751.0000.0340.3850.0000.0510.2740.0390.3410.2250.0730.0220.0840.050
education0.1810.2470.0000.0341.0000.0610.0000.1950.1370.1950.0590.0490.0330.0740.0650.201
frq-0.7630.1530.5410.3850.0611.0000.012-0.1360.811-0.1300.968-0.574-0.0960.0340.131-0.125
gender0.0000.0000.0000.0000.0000.0121.0000.0160.0000.0000.0000.0000.0000.0000.0000.024
house_keeping0.173-0.7160.0970.0510.195-0.1360.0161.000-0.1580.523-0.135-0.0370.0170.2930.0820.508
income-0.9380.2130.5770.2740.1370.8110.000-0.1581.000-0.1570.890-0.730-0.0710.0140.172-0.148
kitchen0.171-0.7200.1130.0390.195-0.1300.0000.523-0.1571.000-0.132-0.0390.0240.2770.0860.521
mnt-0.8490.1680.6150.3410.0590.9680.000-0.1350.890-0.1321.000-0.712-0.1100.0280.138-0.124
per_net_purchase0.7770.0170.7110.2250.049-0.5740.000-0.037-0.730-0.039-0.7121.0000.048-0.1060.132-0.044
rcn0.071-0.0270.0820.0730.033-0.0960.0000.017-0.0710.024-0.1100.0481.0000.0130.0290.012
small_appliances-0.026-0.6480.2550.0220.0740.0340.0000.2930.0140.2770.028-0.1060.0131.0000.0140.281
status0.2080.1250.1900.0840.0650.1310.0000.0820.1720.0860.1380.1320.0290.0141.0000.092
toys0.161-0.7160.1290.0500.201-0.1250.0240.508-0.1480.521-0.124-0.0440.0120.2810.0921.000

Missing values

2025-10-01T13:48:32.170006image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-01T13:48:32.240696image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-01T13:48:32.289436image/svg+xmlMatplotlib v3.9.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

ageincomefrqrcnmntclotheskitchensmall_appliancestoyshouse_keepingdependentsper_net_purchasegendereducationstatusdescription
0194690782.03366140237544103False19MGraduationTogetherTake my money!!
11936113023.032615375513842False9FPhDDivorcedTake my money!!
2199028344.0116944321924124True59MGraduationMarriedKind of OK
3195593571.0261088860101965True35FMasterNaNOK nice!
4195591852.0312611385952844True34FGraduationTogetherTake my money!!
5198222386.01465564724821True67MPhDSingleOK nice!
6196969485.018733457171318True46MGraduationTogetherOK nice!
7196068602.0544418411220True37MGraduationTogetherHorrible
81940109499.0307514013893599False17MGraduationDivorcedOK nice!
9199423846.0815319185517101True39F1st CycleTogetherMeh...
ageincomefrqrcnmntclotheskitchensmall_appliancestoyshouse_keepingdependentsper_net_purchasegendereducationstatusdescription
89881947100928.028611527432012False29FMasterDivorcedTake my money!!
8989194787605.0211882334219351False9M1st CycleWidowKind of OK
8990199528144.0104146114024222True59M1st CycleMarriedOK nice!
89911939126254.0463622313244798False22MGraduationDivorcedTake my money!!
8992195487399.02518375682781<NA>47MGraduationMarriedKind of OK
8993196094367.02818966852134True55F1st CycleSingleTake my money!!
8994197558121.0126615362876True71M2nd CycleSingleMeh...
8995198654292.029721011411136111False31MGraduationTogetherTake my money!!
89961938125962.0387516686122556True45M2nd CycleMarriedTake my money!!
8997199426385.092446513214615True52M1st CycleSingleKind of OK